复杂非线性系统的结构化实验建模

M. Milanese, C. Novara, L. Pivano
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引用次数: 5

摘要

本文提出了一种迭代算法,用于辨识由未知多变量信号相互连接的两个MIMO系统,一个是线性系统,另一个是非线性系统。所考虑的互连结构可以代表Hammerstein、Wiener或Lur’e模型,也可以代表更复杂的结构。该方法的一个关键特点是非线性子系统可以是动态的,不需要具有给定的参数形式。这样就避免了非线性子系统参数化选择的复杂性和精度问题。此外,用于评估识别误差的代价函数随着迭代次数的增加而减小。通过控制悬架车辆垂直动力学仿真半车模型的识别,验证了该算法的有效性。假设实验设置易于在实际汽车上实现,将半车模型分解为一个广义Lur'e系统,由一个线性MIMO系统组成,通过未测信号与一个MIMO非线性动态系统以反馈形式连接。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Structured experimental modeling of complex nonlinear systems
In the paper an iterative algorithm is proposed for the identification of a system composed of two MIMO systems, one linear and the other one nonlinear, interconnected by an unknown multivariable signal. The considered interconnection structure can represent Hammerstein, Wiener or Lur'e models, as well as more complex structures. A key feature of the proposed method is that the nonlinear subsystem may be dynamic and is not supposed to have a given parametric form. In this way the complexity/accuracy problems posed by the proper choice of the suitable parameterization of the nonlinear subsystem are circumvented. Moreover, the cost function used to evaluate identification errors is guaranteed to decrease for increasing number of iterations. The effectiveness of the algorithm is tested on the problem of identifying a simulated half-car model for vertical dynamics of vehicles with controlled suspensions. Assuming an experimental setup easily realizable in actual experiment on real cars, the half-car model is decomposed as a generalized Lur'e system, consisting of a linear MIMO system, connected in a feedback form with a MIMO nonlinear dynamic system through not measured signals.
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